Enabling high-fidelity electrochemical P2D modeling of lithium-ion batteries via fast and non-destructive parameter identification

Physics-based electrochemical models provide insight into the battery internal states and have shown great po-tential in battery design optimization and automotive and aerospace applications. However, the complexity of the electrochemical model makes it difficult to obtain parameter values accurately. In this study, a novel non-destructive parameter identification method is proposed to parameterize the most commonly used electrochem-ical pseudo-two-dimensional model. The whole identification process consists of three key steps. First, in order to find the optimal identification conditions, the sensitivity of model parameters is analyzed, and parameters are classified into three types according to their most sensitive conditions. Second, feasible initial guess values of these unknown parameters are obtained using a deep learning algorithm, which can not only help avoid the divergence problem of the identification algorithm but also speed up the subsequent identification process. Fi-nally, two different approaches are combined and used for parameter identification, and parameters that have high sensitivity are estimated in a step-wise manner. We show that 14 electrochemical parameters can be esti-mated accurately within 1 h using simulation and experimental data. After estimating the model parameters, the root-mean-square error of the predicted voltage from the model is less than 14 mV.

Automated summary

A novel non-destructive parameter identification method is proposed in this study to optimize the electrochemical model, and successful experimental results are demonstrated.